Reversible Action Design for Combinatorial Optimization with Reinforcement Learning

• URL: http://arxiv.org/abs/2102.07210v1
• Date: Sun, 14 Feb 2021 18:05:42 GMT
• Title: Reversible Action Design for Combinatorial Optimization with Reinforcement Learning
• Authors: Fan Yao, Renqin Cai, Hongning Wang
• Abstract summary: Reinforcement learning (RL) has recently emerged as a new framework to tackle these problems. We propose a general RL framework that not only exhibits state-of-the-art empirical performance but also generalizes to a variety class of COPs.
• Score: 35.50454156611722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Combinatorial optimization problem (COP) over graphs is a fundamental challenge in optimization. Reinforcement learning (RL) has recently emerged as a new framework to tackle these problems and has demonstrated promising results. However, most RL solutions employ a greedy manner to construct the solution incrementally, thus inevitably pose unnecessary dependency on action sequences and need a lot of problem-specific designs. We propose a general RL framework that not only exhibits state-of-the-art empirical performance but also generalizes to a variety class of COPs. Specifically, we define state as a solution to a problem instance and action as a perturbation to this solution. We utilize graph neural networks (GNN) to extract latent representations for given problem instances for state-action encoding, and then apply deep Q-learning to obtain a policy that gradually refines the solution by flipping or swapping vertex labels. Experiments are conducted on Maximum $k$-Cut and Traveling Salesman Problem and performance improvement is achieved against a set of learning-based and heuristic baselines.

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